Determining protein structure from electron-density maps using pattern matching | Academic Article individual record
abstract

TEXTAL is an automated system for building protein structures from electron-density maps. It uses pattern recognition to select regions in a database of previously determined structures that are similar to regions in a map of unknown structure. Rotation-invariant numerical values, called features, of the electron density are extracted from spherical regions in an unknown map and compared with features extracted around regions in maps generated from a database of known structures. Those regions in the database that match best provide the local coordinates of atoms and these are accumulated to form a model of the unknown structure. Similarity between the regions in the database and an uninterpreted region is determined firstly by evaluating the numerical difference in feature values and secondly by calculating the electron-density correlation coefficient for those regions with similar feature values. TEXTAL has been successful at building protein structures for a wide range of test electron-density maps and can automatically model entire protein structures in a few hours on a workstation. Models built by TEXTAL from test electron-density maps of known protein structures were accurate to within 0.6-0.7 A root-mean-square deviation, assuming prior knowledge of C(alpha) positions. The system represents a new approach to protein structure determination and has the potential to greatly reduce the time required to interpret electron-density maps in order to build accurate protein models.

author list (cited authors)
Holton, T., Ioerger, T. R., Christopher, J. A., & Sacchettini, J. C.
publication date
2000
keywords
  • Amino Acid Sequence
  • Animals
  • Nerve Tissue Proteins
  • Software
  • Crystallography, X-Ray
  • Molecular Sequence Data
  • Fatty Acid-binding Protein 7
  • Algorithms
  • Carrier Proteins
  • Protein Structure, Secondary
  • Sensitivity And Specificity
  • Proteins
  • Models, Chemical
  • Pattern Recognition, Automated
  • Neoplasm Proteins
  • Fatty Acid-binding Proteins
  • Fatty Acids
  • Myelin P2 Protein
  • Models, Molecular
  • Computational Biology
  • Rats
citation count

32